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Modulation Classification

Using CNN to classify RF modulation data.

Dataset is from: DATA LINK

paper: Over the Air Deep Learning Based Radio Signal Classification

Data Preprocessing

Data is processed. Column data are a two variable label composed of the Modulation and SNR, Row 0 is the binary encoded version of the Modulation and SNR, Row 1 is the actual data, each column is a 2, 128 array of I and Q data for the specified Modulation and SNR in the column label.

Build the CNN

from keras.models import Sequential
from keras.layers import Dense, Activation, Dropout, Conv1D, MaxPooling1D, GlobalAveragePooling1D, Flattenverbose, epochs, batch_size = 1, 256, 1024
n_timesteps, n_features, n_outputs = xTrain.shape[1], xTrain.shape[2], yTrain.shape[1]
print('timesteps=', n_timesteps, 'features=', n_features, 'outputs=', n_outputs)
model = Sequential()
model.add(Conv1D(filters=64, kernel_size=3, activation='relu', input_shape=(n_timesteps, n_features)))
model.add(Conv1D(filters=64, kernel_size=3, activation='relu'))
model.add(Dropout(0.5))
model.add(MaxPooling1D(pool_size=2))
model.add(Flatten())
model.add(Dense(100, activation='relu'))
model.add(Dense(n_outputs, activation='softmax'))
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
#model.compile(RAdam(), loss='categorical_crossentropy', metrics=['accuracy'])
print(model.summary())